| Title | Multi-targets device-free localization based on sparse coding in smart city |
|---|---|
| ID_Doc | 36621 |
| Authors | Zhao, M; Qin, DY; Guo, RL; Xu, GC |
| Title | Multi-targets device-free localization based on sparse coding in smart city |
| Year | 2019 |
| Published | International Journal Of Distributed Sensor Networks, 15, 6 |
| Abstract | With the continuous expansion of the market of device-free localization in smart cities, the requirements of device-free localization technology are becoming higher and higher. The large amount of high-dimensional data generated by the existing device-free localization technology will improve the positioning accuracy as well as increase the positioning time and complexity. The positions required from single target to multi-targets become a further increasing difficulty for device-free localization. In order to satisfy the practical localizing application in smart city, an efficient multi-target device-free localization method is proposed based on a sparse coding model. To accelerate the positioning as well as improve the localization accuracy, a sparse coding-based iterative shrinkage threshold algorithm (SC-IA) is proposed and a subspace sparse coding-based iterative shrinkage threshold algorithm (SSC-IA) is presented for different practical application requirements. Experiments with practical dataset are performed for single-target and multi-targets localization, respectively. Compared with three typical machine learning algorithms: deep learning based on auto encoder, K-nearest neighbor, and orthogonal matching pursuit, experimental results show that the proposed sparse coding-based iterative shrinkage threshold algorithm and subspace sparse coding-based iterative shrinkage threshold algorithm can achieve high localization accuracy and low time cost simultaneously, so as to be more practical and applicable for the development of smart city. |
| https://journals.sagepub.com/doi/pdf/10.1177/1550147719858229 |
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